Sure, but it would also be an IA much smarter than the ones we have now, because you cannot replace a human being with the current technology. You can augment one, making her perform the job of two or more humans before for some tasks, but you cannot replace them all, because the current tech cannot reasonably be used without supervision.
It's not even sure it will reduce the workforce for all of the aforementioned jobs: it's making the same amount of work cost less so it can also increase the demand for the said work to the point it is actually increasing the amount of workers. Like how github and npm increased the developers' productivity so much it drove the developer market up.
Most jobs have a limited demand. Because internal jobs are not the same as products in the marketplace.
Products and services typically require a mix of many kinds of internal parts or tasks to be created or supplied. Most of them are not the majority cost drivers.
You don’t increase the amount of software created by responding to cheaper documentation by increasing the documentation to keep your staff busy, or hiring more document staff, to create even more of the cheaper documentation.
You hire fewer documentation people and shift resources elsewhere.
Making one tasks easier is more likely to reduce internal demand for employees in that area. Very unlikely to somehow increase demand for it.
Unless all tasks get cheaper, or the task is a majority cost driver, and directly spills into obviously lower prices for customers for the product or service.
For the record, labor is around 2/3 of the cost of the product you consume, in any developed economy. And it's not just manufacturing labor (which is a small fraction of that), but all labor. Labor costs have real impact on the price (and then quantity) of product being sold, all over the board.
> Making one tasks easier is more likely to reduce internal demand for employees in that area. Very unlikely to somehow increase demand for it.
And yet we have way more software developers now that you can just use open-source libraries everywhere instead of re-inventing the wheel in a proprietary way every time. This has caused an increased in developer productivity that dwarf any other productivity improvements in other sectors, and yet the number of developers increased.
No they aren't. Some jobs are being scaled down because of the increased productivity of other people with AI, but none of the jobs you listed are within reach of autonomous AI work with today's technology (as illustrated by the AirCanada hilarious case).
I would split the difference and say a bunch of companies are /trying/ to replace workers with LLMs but are finding out, usually with hilarious results, that they are not reliable enough to be left on their own.
However, there are some boosts that can be made to augment the performance of other workers if they are used carefully and with attention to detail.
Doesn't the Air Canada case demonstrate the exact opposite, that real businesses actually are using AI today to replace jobs that previously would have required a human?
Furthermore, don't you think it's possible for a real human customer service agent to make such a blunder as what happened in that case?
Possibly, a human customer rep. could make a mistake, but said human could correct the mistake quickly. The only responses I've had from "A.I" upon notifying it of its own mistake, is endless apologies. No corrections.
Anyone experienced ability to self-correct from an "A.I" ?
> Doesn't the Air Canada case demonstrate the exact opposite, that real businesses actually are using AI today to replace jobs that previously would have required a human?
It shows that some are trying, and failing at that.
> Furthermore, don't you think it's possible for a real human customer service agent to make such a blunder as what happened in that case?
One human? Sure, some people are plain dumb. The thing is you don't give your entire customer service under the responsibility of a single dumb human. You have thousands of them and only a few of them could do the same mistake. When using LLMs, you're not gonna use thousands of different LLMs so such mistakes can have an impact that's multiple order of magnitude higher.